Biology subjects

Taylor, M. L.

Publications and source records attributed to Taylor, M. L..

8 recordsLinked to original sources

bioRxiv2026

Genomic and oceanographic evidence of reef connectivity across the western and central Indian Ocean

Connectivity among coral populations, via larval dispersal, is one of the key mechanisms supporting reef recovery after climate-driven bleaching events. This study examines reef connectivity of remote atolls of the understudied central and western Indian Ocean seascape, focusing on a key reef-building species, Acropora aff. tenuis. Using genomic data from ultra-conserved elements alongside particle-tracking simulations, we investigate patterns of population structure and gene flow, and the influence of ocean currents. Results show asymmetrical connectivity: strong gene flow occurs within the Chagos Archipelago and between Chagos and the Inner Seychelles, highlighting their importance as regional larval sources. In contrast, the Outer Seychelles mainly functions as a larval sink, and IIes Glorieuses appears to be the least connected. Whilst population structure suggests a partially permeable dispersal barrier between the Inner and Outer Seychelles. Differences between genetic patterns and simulated ocean-driven dispersal suggest that ocean currents are not the only important driver of connectivity patterns. Additional factors, such as stepping-stone dispersal among atolls, fine-scale oceanographic processes, and species biology likely play important roles. Overall, we find widespread genomic connectivity across this seascape, which has positive implications for reef resilience, though future resilience will depend on frequency and severity of future bleaching events.

ecology↗

bioRxiv2026

Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes

Understanding and modeling how a single human genome concurrently encodes gene regulatory programs for thousands of cell types remains a central challenge in genomics and machine learning. Most human cell types emerge during embryonic, fetal, and pediatric development which are inaccessible to comprehensive molecular profiling. To circumvent this, we hypothesized that the mismatch in evolutionary rates between cis-acting enhancers that modulate gene expression (fast) and the trans-acting regulatory factors that specify cell types (slow) creates an opportunity for evolutionary transfer learning. Specifically, models trained to predict cell type-specific enhancers in one species should generalize to the orthologous cell types and enhancers of related species. To test this, we generated a single-cell atlas of chromatin accessibility spanning mouse embryonic day 10 (E10) to birth (P0). Using combinatorial indexing1, we profiled 3.9 million nuclei from 36 staged embryos, resolving genome-wide accessibility in 36 cell classes and 140 cell types. We then trained a series of multi-output deep learning models (CREsted2), each addressing limitations of the preceding approach, towards the goal of genome-wide prediction of distal enhancers across major developmental lineages. An evolution-naive model achieved strong performance on heldout peaks, but exhibited two failure modes during genome-wide inference: overprediction at tandem repeats and conflation of promoter and distal enhancer grammars. An evolution-aware model resolved these by regrouping accessible regions based on their retention and functional coherence across mammalian evolution, but failed to generalize across species. Finally, an evolution-augmented model, STEAM (Synteny-aware Transfer learning for Enhancer Activity Modeling), incorporated enhancer orthologs from 241 mammalian genomes (Zoonomia3) in a synteny-supervised manner. This increased the effective data scale by as much as 195-fold, markedly improving generalization across mammals despite greater label noise. We applied STEAM to the genome-wide inference of cell class-specific distal developmental enhancers in humans, mice (HumMus) and 239 additional mammals3 (BabaGanoush), i.e. 32 x 241 = 7,712 genome-wide distal enhancer prediction tracks. Together, our results unify advances in single-cell profiling, deep learning, and comparative genomics into a framework for the evolutionary transfer learning of noncoding regulatory grammars. More broadly, our work supports the view that model organisms and evolutionarily diverse genomes are indispensable resources for accelerating and enhancing the AI-enabled exploration of human biology. NoteAn interactive version of this preprint, together with count matrices, CREsted models, prediction tracks, code and reproducible figures, is available at this link (ref 4).

genomics↗

bioRxiv2025

Distinct modes of evolution drive HIV escape from two broadly neutralizing antibodies.

Broadly neutralizing antibodies (bNAbs) show promise for HIV treatment and prevention, but are vulnerable to resistance evolution. Comprehensively understanding in vivo viral escape from individual bNAbs is necessary to design bNAb combinations that will provide durable responses. We characterize viral escape from two such bNAbs, 10-1074 and 3BNC117, using deep, longitudinal sequencing of full length HIV envelope (env) genes from study participants treated with bNAb monotherapy. Improved sequencing depth and computational evolutionary analyses permit us to identify in vivo routes and parallelism underlying HIV escape from each bNAb, providing new insights into this evolutionary process. We find that 10-1074 escape is restricted to a small number of previously documented pathways seen across participants, but these escape mutations 1) emerge via extensively recurrent mutation, 2) are not equally preferred, and 3) can pre-exist at low frequency in intra-host viral populations before therapy, although their detection does not predict rebound timing. In contrast, 3BNC117 escape follows background-specific patterns in which specific escape mutations present in one intra-host population rarely emerge or spread in other populations, except among highly related viruses. Despite this, 3BNC117 escape mutations often still emerge recurrently within their host. Our findings map longitudinal in vivo antibody escape across 20 diverse clade B HIV intra-host populations and reveal clinically relevant resistance dynamics that highlight how combination bNAb therapies will need to contend with extensively recurring escape mutations and dependence on genetic background. Significance StatementUsing recently developed techniques that capture viral genetic diversity and associations between mutations at depth, we deeply sequenced HIV from two clinical trials of broadly neutralizing antibody (bNAb) monotherapies, 3BNC117 and 10-1074. We computationally characterized HIV populations longitudinally with unprecedented resolution as they escaped these therapies in people living with HIV. Intra-host tracking of individual HIV genetic backgrounds reveals extensively recurrent mutations driving escape and suggests that HIV escape routes from certain bNAbs can depend sensitively on the genetic background of the virus. Our findings highlight the difficulties in evaluating pre-treatment resistance, provide an analysis blueprint for future trials, and inform the design of emerging combination antibody therapies to maximize the likelihood of durable efficacy.

molecular biology↗

bioRxiv2025

Epigenomic insights into extreme longevity in the world's oldest terrestrial animal, Jonathan

Giant tortoises exhibit exceptional longevity, often exceeding the human lifespan. To understand the genomic and epigenomic basis of their longevity, awe analyzed the DNA sequence and methylome of Jonathan, an Aldabra giant tortoise (Aldabrachelys gigantea), estimated to be 192 years old. Relative to other giant tortoises (Aldabrachelys gigantea and Chelonoidis abingdonii), we found Jonathan has gene variants in pathways associated with aging, including DNA repair and telomere regulation. Consistent with his advanced age, Jonathan has significant age-related changes in DNA methylation and methylation entropy, compared with a 5-year-old Aldabra individual. Notably, we found that low entropy regions in Jonathans methylome were enriched for genes involved in the electron transport chain. This suggests that high-fidelity transcription of these genes may be crucial for extreme longevity. With this data, we propose a model for aging, that links efficient mitochondrial energy production with nuclear maintenance of low methylation entropy.

genomics↗

bioRxiv2024

Diversified, miniaturized and ancestral parts for mammalian genome engineering and molecular recording

As the synthetic biology and genome engineering fields mature and converge, there is a clear need for a "parts list" of components that are diversified with respect to both functional activity (to facilitate design) and primary sequence (to facilitate assembly). Here we designed libraries composed of extant, ancestral, mutagenized or miniaturized variants of Pol III promoters or guide RNA (gRNA) scaffolds and quantified their ability to mediate precise edits to the mammalian genome via multiplex prime editing. We identified thousands of parts that reproducibly drive a range of editing activities in human and mouse stem cells and cancer cell lines, including hundreds exhibiting similar or greater activity than the sequences used in conventional genome engineering constructs. We further conducted saturation mutagenesis screens of canonical Pol III promoters (U6p, 7SKp, H1p) and the prime editing guide RNA (pegRNA) scaffold, which identified tolerated variants that can be superimposed on baseline parts to further enhance sequence diversity. While characterizing thousands of orthologous promoters from hundreds of extant or ancestral genomes, we incidentally mapped the functional landscape of mammalian Pol III promoter evolution. Finally, to showcase the usefulness of these parts, we designed a "ten key" molecular recording array that lacks repetitive subsequences in order to facilitate its one-step assembly in yeast. Upon delivering this 15.8 kb tandem array of promoters and guides to mammalian cells, individual pegRNAs exhibited balanced activities as predicted by the activity of component parts, despite their relocation to a single locus. Looking forward, we anticipate that the diversified parts and variant effect maps reported here can be leveraged for the design, assembly and deployment of synthetic loci encoding arrays of gRNAs exhibiting predictable, differentiated levels of activity, which will be useful for multiplex perturbation, advanced biological recorders and complex genetic circuits.

synthetic biology↗

bioRxiv2023

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-protein Binding Affinity

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes and that require proteins interact with sufficient binding affinity (BA) to form stable or transient complexes. Despite advancements in our understanding of protein-protein binding, much remains unknown about the interfacial region and its association with BA. Here we investigate the correlation of residue and atomic contacts of different types with BA and reveal the impact of the specific amino acids at the binding interface on BA. We create a series of linear regression (LR) models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids in contacts at the protein-protein interface. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different datasets, indicating the importance of the identities of interfacial amino acids in underlying the strength of BA. When trained on a diverse set of 141 complexes from two benchmark datasets, the best performing BA model (Pearson correlation coefficient R=0.68) was generated with an explicit linear equation involving six amino acids (tyrosine, glycine, serine, arginine, valine, and isoleucine). Tyrosine, in particular, was identified as the key amino acid in the quantitative link between specific amino acids and BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine, serine, and arginine were identified as the next three most important amino acids in predicting BA. The results from this study further our understanding of the importance of specific amino acids in PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

biophysics↗

bioRxiv2023

A single-cell transcriptional timelapse of mouse embryonic development, from gastrula to pup

The house mouse, Mus musculus, is an exceptional model system, combining genetic tractability with close homology to human biology. Gestation in mouse development lasts just under three weeks, a period during which its genome orchestrates the astonishing transformation of a single cell zygote into a free-living pup composed of >500 million cells. Towards a global framework for exploring mammalian development, we applied single cell combinatorial indexing (sci-*) to profile the transcriptional states of 12.4 million nuclei from 83 precisely staged embryos spanning late gastrulation (embryonic day 8 or E8) to birth (postnatal day 0 or P0), with 2-hr temporal resolution during somitogenesis, 6-hr resolution through to birth, and 20-min resolution during the immediate postpartum period. From these data (E8 to P0), we annotate dozens of trajectories and hundreds of cell types and perform deeper analyses of the unfolding of the posterior embryo during somitogenesis as well as the ontogenesis of the kidney, mesenchyme, retina, and early neurons. Finally, we leverage the depth and temporal resolution of these whole embryo snapshots, together with other published data, to construct and curate a rooted tree of cell type relationships that spans mouse development from zygote to pup. Throughout this tree, we systematically nominate sets of transcription factors (TFs) and other genes as candidate drivers of the in vivo differentiation of hundreds of mammalian cell types. Remarkably, the most dramatic shifts in transcriptional state are observed in a restricted set of cell types in the hours immediately following birth, and presumably underlie the massive changes in physiology that must accompany the successful transition of a placental mammal to extrauterine life.

developmental biology↗

bioRxiv2018

Molecular epidemiology of Colombian Histoplasma capsulatum isolates shows their polyphyletic behavior and point out raw chicken manure as one of the infections sources

The thermally dimorphic fungus Histoplasma capsulatum is the causative agent of histoplasmosis, which is the most prevalent endemic mycosis in America. The replacement of organic matter in agro-ecosystems is necessary in the tropics, and the use of organic fertilizers has increased. Cases and outbreaks due to the presence of the fungus in these components have been reported. In Colombia, chicken manure is the most common raw material in the organic fertilizers production. In this work, we reached the isolation of the fungus from a chicken manure. Then, we were able to compare genetically 3 environmental isolates with 42 Colombian human clinical isolates. The genetic comparison showed the environmental isolates grouping together with the clinical isolates. This result suggests chicken manure as one of the infection source with H. capsulatum. Also, the phylogenetic analysis using another H. capsulatum isolates from databases showed that the Colombian isolates widely distributed in the relation tree. This result pointed out the great genetic diversity among H. capsulatum Colombian population.

microbiology↗